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Record W1988588900 · doi:10.1680/dare.2009.19.3.133

Report on the European working group on internal erosion, St. Petersburg

2009· article· en· W1988588900 on OpenAlexaboutno aff
Alan Brown, Rodney Bridle

Bibliographic record

VenueDams and Reservoirs · 2009
Typearticle
Languageen
FieldEngineering
TopicDam Engineering and Safety
Canadian institutionsnot available
Fundersnot available
KeywordsCzechSt petersburgEngineeringWork (physics)Library scienceRelevance (law)Working groupCivil engineeringForensic engineeringRussian federationGeographyPolitical scienceMechanical engineeringRegional scienceLaw

Abstract

fetched live from OpenAlex

This article reports on the seventh meeting of the working group, which was held on 27–29 April 2009 in St. Petersburg, hosted by VNIIG institute. There were 28 attendees from the UK, Canada, France, Sweden, Austria, Netherlands, Czech Republic and Poland and 12 attendees from Russia comprising, as in previous meetings, a mixture of researchers, practising engineers and dam safety managers. The workshop included 27 presentations and a visit to the hydraulic and soils laboratory of VNIIG. The participants included the authors of the current paper, which summarises the main points of relevance to dam engineers in the UK. The slide presentations are available on the VNIIG website http://www.vniig.ru/en/news/news25.htm . Selected summaries of work that had progressed since the intermediate report published at the European conference in Freising in September 2007 and presented in 2008 at Obergurgl in Austria and in 2009 at St. Petersburg are noted in the paper. VNIIG is a Russian research and development institute which carries out analysis and model testing in the fields of hydraulics, soil mechanics, concrete materials and safety of existing structures. One of its main clients is RusHydro, which was founded in 1921 and is the second largest generator in the world in terms of installed capacity, with 49 plants and 24 GW installed capacity.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.427
Threshold uncertainty score0.344

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.013
GPT teacher head0.210
Teacher spread0.198 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2009
Admission routes1
Has abstractyes

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